How Much Should We Pay for Mental Health Deterioration? The Subjective Monetary Value of Mental Health After the 27F Chilean Earthquake


In this article we use the life satisfaction approach to assess the non-pecuniary costs of Mental health impacts after the 2010 Chile earthquake. By linking both subjective well-being valuation literature with studies that describe psychological impacts after natural disasters, we are able to quantify how big a compensation should be to leave individuals as well as they were before the event. Our results suggest that people who experience stress should be compensated by approximately 80–90% of the average monthly income if the shock was strong enough to cause significant damages. These estimates are robust to different empirical specifications, endogeneity, shock intensity measures, and mental health definitions. We estimate that the total costs of mental health deterioration are about 7% of the total reported damages, a significant amount that policymakers should not ignore in post-earthquake reconstruction stages.

This is a preview of subscription content, log in to check access.

Fig. 1

Source: USGS shakemaps

Fig. 2

Source: Own elaboration

Fig. 3

Source: Own elaboration based on USGS shakemaps

Fig. 4

Source: Own elaboration

Fig. 5

Source: Own elaboration


  1. 1.

    8.8\(^{\circ }\) in the Moment magnitude scale (MMS).

  2. 2.

    Similar evidence has been found for other environmental disasters such as floods (Luechinger and Raschky 2009; Sekulova et al. 2016), droughts (Carroll et al. 2009), extreme weather events (von Möllendorff and Hirschfeld 2016), and hurricanes (Berlemann 2016).

  3. 3.

    The Life Satisfaction Approach has also been used to value intangibles as diverse as airport noise (Van Praag and Baarsma 2005) and terrorism (Frey et al. 2009).

  4. 4.

    For a further review of welfare measures in economics see Bockstael and Freeman (2005). Van Praag and Baarsma (2005), Welsch and Kühling (2009) and Frey et al. (2009) provide good examples of how these measures can be used under the LSA to compute the monetized value of intangible goods.

  5. 5.

    Another approach to compute the non-pecuniary costs of an earthquake would be to compute a Quality of Life Index using hedonic price models as in Naoi et al. (2007).

  6. 6.

    Appendix 2: Derivation of Welfare Measures” section presents the derivation of Eq. (2).

  7. 7.

    Good controls are those likely to have an effect on mental health and income, but that are not themselves affected by mental health, income or the earthquake/tsunami intensity (Angrist and Pischke 2008). Thus, we refrain to include any variables measured after the earthquake took place as controls.

  8. 8.

    See “Appendix 2: Derivation of Welfare Measures” section.

  9. 9.

    Even though the immediate shock was unexpected, the whole country is known for frequent earthquake activity that could have affected past firm and household decisions. Fortunately, since every region in Chile has had major earthquakes in the past, there is no credible possibility of self-sorting into “earthquake-free zones”, therefore we have a strong case to claim exogeneity at least for our earthquake/tsunami measures.

  10. 10.

    The sample can be downloaded at

  11. 11.

    The regions affected by 27-F earthquake are Valparaiso, Metropolitana, Libertador Bernando O’Higgins, Maule, Biobio and Araucania. See Fig. 1.

  12. 12.

    They correspond to the 6.1% of the raw sample.

  13. 13.

    One of the reviewers raised the concern of the potential relationship between the attrition and the negative experiences related to the 2010 earthquake. We regress the non-response dummy variable on some earthquake intensities controlling for several factors using a Probit Model. The results, available upon request, show no significant association between the non-response and the exposure to the earthquake.

  14. 14.

    Although the total sample of the CASEN-PT survey corresponds to 62,194 individuals, only the individuals present in the interview (21,603) answered the questions related to PTSD.

  15. 15.

    The question was: “In the last 30 days have you have a health problem?”.

  16. 16.

    PGA is often a good predictor of both fatal and nonfatal injuries (Peek-Asa et al. 2003; Mahue-Giangreco et al. 2001).

  17. 17.

    The affected area corresponds to 150 municipalities, which range from entirely rural areas to big cities containing both urban and rural areas.

  18. 18.

    We choose to use the inverse of the DTS to maintain the same interpretation as in our theoretical model presented in Sect. 3.

  19. 19.

    To take into account of a potential correlation of residuals when individuals are taken from the same city, cluster standard errors at the city level are used in all specifications.

  20. 20.

    We also test whether income has a diminishing marginal effect on mental health. Table 9 in “Appendix 1: Additional Tables and Figures” section show that, at least for this sample, the relationship between income and mental health is linear.

  21. 21.

    An important control is whether the individuals are religious-minded and have social capital (Ali et al. 2012). However, our survey data does not provide detailed information about these dimensions.

  22. 22.

    This contrasts with the results for the earthquake in Pakistan found by Ali et al. (2012), which show that being the head of the family is one of the strongest predictors of PTSD.

  23. 23.

    Note that this contradicts the hypothesis that individuals with better social networks are more resilient.

  24. 24.

    Standard errors were clustered at the city level in the Probit Model. Partial effects were computed at the mean of the variables, whereas standard errors were computed using Delta method. The marginal impact for dummy variables is the discrete change from the reference level.

  25. 25.

    For the models estimated by the Maximum Likelihood procedure, such as the IV-Probit and IV-Ordered Probit model, the weak-instrument test is carried out by testing whether the instrument is significant in the reduced equation which is estimated jointly with the mental health equation. Since our model is just identified, the \(\chi ^2\)-squared statistic is approximately similar to the F-statistic.

  26. 26.

    Here we run the following model:

    $$\begin{aligned} h_{i1}^* = \alpha + \beta \ln (y_{i0})+ \gamma r_i + {\mathbf{x}}_{i0}'{\varvec{\delta }}+ \lambda z_i + \epsilon _{i0}, \end{aligned}$$

    and test whether \(\lambda = 0\). This test has been used to test the validity of the exclusion restriction, i.e., \({\text {Cov}}(z_{i0}\cdot \epsilon _{i0}) = 0\). See for example Kan (2007).

  27. 27.

    For the 2SLS, the exogeneity of income is tested using Wooldridge (1995)’s robust score test. For the IV-Probit and IV-Ordered Probit model the exogeneity test corresponds to \(H_0:\rho = 0\), where \(\rho\) is the correlation between the error terms of the mental health’s and income’s equation.

  28. 28.

    Table 10 shows that the estimates of the compensatory variations are not sensitive if we consider that the individuals affected by the earthquake were those who experienced an intensity equal to or greater than 6.5 on the MMI scale.


  1. Abeldaño, R., Fernández, R., Estario, J., & Enders, J. (2013). Distribución espacial de los trastornos de estrés postraumático en Chile a partir del terremoto del 27-f. Revista de Salud Pública, 17(3), 40–46.

    Google Scholar 

  2. Akashah, M., & Marks, S. P. (2006). Accountability for the health consequences of human rights violations: Methodological issues in determining compensation. Health and Human Rights, 9, 256–279.

    Article  Google Scholar 

  3. Alexander, D. (1997). The study of natural disasters, 1977–97: Some reflections on a changing field of knowledge. Disasters, 21(4), 284–304.

    Article  Google Scholar 

  4. Ali, M., Farooq, N., Bhatti, M. A., & Kuroiwa, C. (2012). Assessment of prevalence and determinants of posttraumatic stress disorder in survivors of earthquake in Pakistan using Davidson Trauma Scale. Journal of Affective Disorders, 136(3), 238–243.

    Article  Google Scholar 

  5. Andrades, M., García, F. E., Calonge, I., & Martínez-Arias, R. (2018). Posttraumatic growth in children and adolescents exposed to the 2010 earthquake in Chile and its relationship with rumination and posttraumatic stress symptoms. Journal of Happiness Studies, 19, 1–13.

    Article  Google Scholar 

  6. Andrades, M., García, F. E., Reyes-Reyes, A., Martínez-Arias, R., & Calonge, I. (2016). Psychometric properties of the posttraumatic growth inventory for children in Chilean population affected by the earthquake of 2010. American Journal of Orthopsychiatry, 86(6), 686.

    Article  Google Scholar 

  7. Angrist, J. D., & Pischke, J.-S. (2008). Mostly harmless econometrics: An empiricist’s companion. Princeton: Princeton University Press.

    Google Scholar 

  8. Baryshnikova, N., & Pham, N. (2018). Heterogeneity in the relationship between natural disasters and mental health: A quantile approach. Technical report.

  9. Berlemann, M. (2016). Does hurricane risk affect individual well-being? Empirical evidence on the indirect effects of natural disasters. Ecological Economics, 124, 99–113.

    Article  Google Scholar 

  10. Bilger, M., & Carrieri, V. (2013). Health in the cities: When the neighborhood matters more than income. Journal of Health Economics, 32(1), 1–11.

    Article  Google Scholar 

  11. Böckerman, P., Johansson, E., & Saarni, S. I. (2011). Do established health-related quality-of-life measures adequately capture the impact of chronic conditions on subjective well-being? Health Policy, 100(1), 91–95.

    Article  Google Scholar 

  12. Bockstael, N. E., & Freeman, A. M. (2005). Chapter 12 welfare theory and valuation. In K.-G. Mler & J. R. Vincent (Eds.), Valuing environmental changes volume 2 of handbook of environmental economics (pp. 517–570). Amsterdam: Elsevier.

    Google Scholar 

  13. Carroll, N., Frijters, P., & Shields, M. A. (2009). Quantifying the costs of drought: New evidence from life satisfaction data. Journal of Population Economics, 22(2), 445–461.

    Article  Google Scholar 

  14. Clark, A. E., & Oswald, A. J. (2002). A simple statistical method for measuring how life events affect happiness. international Journal of Epidemiology, 31(6), 1139–1144.

    Article  Google Scholar 

  15. Cova, F., & Rincon, P. (2010). The mental health consecuences of the 27f earthquake and tsunami. Terapia Psicológica, 28(2), 179–185.

    Article  Google Scholar 

  16. Danzer, A. M., & Danzer, N. (2016). The long-run consequences of chernobyl: Evidence on subjective well-being, mental health and welfare. Journal of Public Economics, 135, 47–60.

    Article  Google Scholar 

  17. Davidson, J. R., Book, S., Colket, J., Tupler, L., Roth, S., David, D., et al. (1997). Assessment of a new self-rating scale for post-traumatic stress disorder. Psychological Medicine, 27(1), 153–160.

    Article  Google Scholar 

  18. Davidson, J., Kudler, H., & Smith, R. (1987). Personality in chronic post-traumatic stress disorder: A study of the eysenck inventory. Journal of Anxiety Disorders, 1(4), 295–300.

    Article  Google Scholar 

  19. Davidson, J. R., Tharwani, H. M., & Connor, K. M. (2002). Davidson trauma scale (DTS): Normative scores in the general population and effect sizes in placebo-controlled SSRI trials. Depression and Anxiety, 15(2), 75–78.

    Article  Google Scholar 

  20. Ettner, S. L. (1996). New evidence on the relationship between income and health. Journal of Health Economics, 15(1), 67–85.

    Article  Google Scholar 

  21. Figueroa, R. A., González, M., & Torres, R. (2010). Plan de reconstrucción psicológica post terremoto. Revista médica de Chile, 138(7), 920–921.

    Article  Google Scholar 

  22. Freedy, J. R., Kilpatrick, D. G., & Resnick, H. S. (1993). Natural disasters and mental health: Theory, assessment, and intervention. Journal of Social Behavior and Personality, 8(5), 49.

    Google Scholar 

  23. Frey, B. S., Luechinger, S., & Stutzer, A. (2009). The life satisfaction approach to valuing public goods: The case of terrorism. Public Choice, 138(3–4), 317–345.

    Article  Google Scholar 

  24. Galea, S., Nandi, A., & Vlahov, D. (2005). The epidemiology of post-traumatic stress disorder after disasters. Epidemiologic Reviews, 27(1), 78–91.

    Article  Google Scholar 

  25. García, F. E., Páez-Rovira, D., Zurtia, G. C., Martel, H. N., & Reyes, A. R. (2014). Religious coping, social support and subjective severity as predictors of posttraumatic growth in people affected by the earthquake in Chile on 27/2/2010. Religions, 5(4), 1132–1145.

    Article  Google Scholar 

  26. Ishino, T., Kamesaka, A., Murai, T., & Ogaki, M. (2012). Effects of the great east Japan earthquake on subjective well-being. Journal of Behavioral Economics and Finance, 5, 269–272.

    Google Scholar 

  27. Jara, B., & Faggian, A. (2018). Chapter 8: Labor market resilience and reorientation in disaster scenarios. In T. Baycan & H. Pinto (Eds.), Resilience, crisis and innovation dynamics, volume 2 of new horizons in regional science (pp. 153–168). Cheltenham: Edward Elgar.

    Google Scholar 

  28. Johansson, P.-O. (1987). The economic theory and measurement of environmental benefits. Cambridge: Cambridge University Press.

    Google Scholar 

  29. Kalia, M. (2002). Assessing the economic impact of stress [mdash] the modern day hidden epidemic. Metabolism-Clinical and Experimental, 51(6), 49–53.

    Article  Google Scholar 

  30. Kan, K. (2007). Cigarette smoking and self-control. Journal of Health Economics, 26(1), 61–81.

    Article  Google Scholar 

  31. Kessler, R. C. (2000). Posttraumatic stress disorder: The burden to the individual and to society. The Journal of Clinical Psychiatry, 5, 4–12.

    Google Scholar 

  32. Leiva-Bianchi, M. C., & Araneda, A. C. (2013). Validation of the Davidson Trauma Scale in its original and a new shorter version in people exposed to the f-27 earthquake in Chile. European Journal of Psychotraumatology, 4(1), 21239.

    Article  Google Scholar 

  33. Levinson, A. (2012). Valuing public goods using happiness data: The case of air quality. Journal of Public Economics, 96(9–10), 869–880.

    Article  Google Scholar 

  34. Lindahl, M. (2005). Estimating the effect of income on health and mortality using lottery prizes as an exogenous source of variation in income. Journal of Human Resources, 40(1), 144–168.

    Article  Google Scholar 

  35. Lomnitz, C. (2004). Major earthquakes of Chile: A historical survey, 1535–1960. Seismological Research Letters, 75(3), 368–378.

    Article  Google Scholar 

  36. Luechinger, S. (2009). Valuing air quality using the life satisfaction approach. The Economic Journal, 119(536), 482–515.

    Article  Google Scholar 

  37. Luechinger, S., & Raschky, P. A. (2009). Valuing flood disasters using the life satisfaction approach. Journal of Public Economics, 93(3–4), 620–633.

    Article  Google Scholar 

  38. Mahue-Giangreco, M., Mack, W., Seligson, H., & Bourque, L. B. (2001). Risk factors associated with moderate and serious injuries attributable to the 1994 northridge earthquake, Los Angeles, California. Annals of Epidemiology, 11(5), 347–357.

    Article  Google Scholar 

  39. Naoi, M., Sumita, K., & Seko, M. (2007). Earthquakes and the quality of life in Japan. Journal of Property Research, 24(4), 313–334.

    Article  Google Scholar 

  40. Ohtake, F., & Yamada, K. (2013). Appraising the unhappiness due to the great east Japan earthquake: Evidence from weekly panel data on subjective well-being. Technical report.

  41. Peek-Asa, C., Ramirez, M., Seligson, H., & Shoaf, K. (2003). Seismic, structural, and individual factors associated with earthquake related injury. Injury Prevention, 9(1), 62–66.

    Article  Google Scholar 

  42. Peek-Asa, C., Ramirez, M. R., Shoaf, K., Seligson, H., & Kraus, J. F. (2000). Gis mapping of earthquake-related deaths and hospital admissions from the 1994 Northridge, California, earthquake. Annals of Epidemiology, 10(1), 5–13.

    Article  Google Scholar 

  43. Phifer, J. F., Kaniasty, K. Z., & Norris, F. H. (1988). The impact of natural disaster on the health of older adults: A multiwave prospective study. Journal of Health and Social Behavior, 1, 65–78.

    Article  Google Scholar 

  44. Powdthavee, N. (2008). Putting a price tag on friends, relatives, and neighbours: Using surveys of life satisfaction to value social relationships. The Journal of Socio-Economics, 37(4), 1459–1480.

    Article  Google Scholar 

  45. Ramirez, M., & Peek-Asa, C. (2005). Epidemiology of traumatic injuries from earthquakes. Epidemiologic Reviews, 27(1), 47–55.

    Article  Google Scholar 

  46. Rehdanz, K., Welsch, H., Narita, D., & Okubo, T. (2015). Well-being effects of a major natural disaster: The case of Fukushima. Journal of Economic Behavior and Organization, 116, 500–517.

    Article  Google Scholar 

  47. Sav, G. T. (1974). Natural disasters: Some empirical and economic considerations. USA: National Bureau of Standards.

    Google Scholar 

  48. Sayer, N. A., Spoont, M., & Nelson, D. (2004). Veterans seeking disability benefits for post-traumatic stress disorder: Who applies and the self-reported meaning of disability compensation. Social Science and Medicine, 58(11), 2133–2143.

    Article  Google Scholar 

  49. Sekulova, F., Van den Bergh, J., et al. (2016). Floods and happiness: Empirical evidence from Bulgaria. Ecological Economics, 126(C), 51–57.

    Article  Google Scholar 

  50. Shinfuku, N. (1996). Psychological consequences of the Great Hanshin earthquake. Stress Science, 10, 25–30.

    Google Scholar 

  51. Shinfuku, N. (2011). Disaster psychiatry: Lessons learned from the Great Hanshin Awaji earthquake. Taiwanese Journal of Psychiatry, 25, 3–12.

    Google Scholar 

  52. Van Praag, B. M., & Baarsma, B. E. (2005). Using happiness surveys to value intangibles: The case of airport noise. The Economic Journal, 115(500), 224–246.

    Article  Google Scholar 

  53. Vitriol, V., Cancino, A., Riquelme, P., & Reyes, I. (2013). Earthquake in Chile: Acute stress and post traumatic stress disorder among women in treatment for severe depression. Revista medica de Chile, 141(3), 338–344.

    Article  Google Scholar 

  54. von Möllendorff, C., & Hirschfeld, J. (2016). Measuring impacts of extreme weather events using the life satisfaction approach. Ecological Economics, 121, 108–116.

    Article  Google Scholar 

  55. Wald, D. J., Worden, B. C., Quitoriano, V., & Pankow, K. L. (2005). Shakemap manual: Technical manual, user’s guide, and software guide. Technical report.

  56. Wald, D. J., Quitoriano, V., Heaton, T. H., & Kanamori, H. (1999). Relationships between peak ground acceleration, peak ground velocity, and modified Mercalli intensity in California. Earthquake Spectra, 15(3), 557–564.

    Article  Google Scholar 

  57. Welsch, H., & Kühling, J. (2009). Using happiness data for environmental valuation: Issues and applications. Journal of Economic Surveys, 23(2), 385–406.

    Article  Google Scholar 

  58. Wooldridge, J. M. (1995). Score diagnostics for linear models estimated by two stage least squares. In Advances in econometrics and quantitative economics: Essays in honor of Professor CR Rao (pp. 66–87).

  59. Worden, C., Gerstenberger, M., Rhoades, D., & Wald, D. (2012). Probabilistic relationships between ground-motion parameters and modified Mercalli intensity in California. Bulletin of the Seismological Society of America, 102(1), 204–221.

    Article  Google Scholar 

  60. Wu, Z., Xu, J., & Sui, Y. (2016). Posttraumatic stress disorder and posttraumatic growth coexistence and the risk factors in Wenchuan earthquake survivors. Psychiatry Research, 237, 49–54.

    Article  Google Scholar 

  61. Yamamura, E. (2012). Natural disasters and their long-term effect on happiness: The case of the great Hanshin-Awaji earthquake. Germany: MPRA paper, University Library of Munich.

    Google Scholar 

  62. Zhu, H., Deng, Y., Zhu, R., & He, X. (2016). Fear of nuclear power? Evidence from Fukushima nuclear accident and land markets in China. Regional Science and Urban Economics, 60, 139–154.

    Article  Google Scholar 

  63. Zubizarreta, J. R., Cerdá, M., & Rosenbaum, P. R. (2013). Effect of the 2010 Chilean earthquake on posttraumatic stress reducing sensitivity to unmeasured bias through study design. Epidemiology (Cambridge, Mass.), 24(1), 79.

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Mauricio Sarrias.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Appendix 1: Additional Tables and Figures

See Tables 9, 10, 11 and 12 and Figs. 6 and 7.

Fig. 6

Correlation across PGA, PGV and MMI. Notes: Own elaboration based on USGS shakemaps

Fig. 7

Source: Own elaboration

Compensating variation across models (one standard deviation increase). Notes: Compensating variation were computed using Eq. (5). SE computed using Delta Method and clustered standard errors

Table 9 Sensitivity analysis for earthquake-intensity measures (OLS)
Table 10 Compensating variation for being affected by earthquake (MMI)
Table 11 Instrumental variable estimation (PGA)
Table 12 Instrumental variable estimation (tsunami)

Appendix 2: Derivation of Welfare Measures

Equation (2) can be obtained in the following way. Total differentiation of Eq. (1) gives:

$$\begin{aligned} d\upsilon = \frac{\partial h}{\partial r}dr + \frac{\partial h}{\partial y}dy + \frac{\partial h}{\partial x}dx + \frac{\partial h}{\partial \epsilon }d\epsilon , \end{aligned}$$

Setting \(d \upsilon = 0\) and holding x and \(\epsilon\) constant yields:

$$\begin{aligned} \frac{\partial h}{\partial r}dr + \frac{\partial h}{\partial y}dy = 0. \end{aligned}$$

Solving for \(\frac{d y}{d r}\) gives Eq. (2). Note that in our model income is in logarithm, thus using the fact that \(d \ln (y)= dy/ y\) yields Eq. (5).

Equation (6) is derived as follows. Considering Eqs. (3) and (4), we get:

$$\begin{aligned} \begin{aligned} h(y_{i0};\,r_{i0};\,{\mathbf{x}}_{i0})&= h(y_{i0} - CV;\, r_{i1}, {\mathbf{x}}_{i0}) \\ \alpha + \beta \ln (y_{i0}) + \gamma r_{i0}&= \alpha + \beta \ln (y_{i0} - CV) + \gamma r_{i1} \\ \gamma (r_{i0} - r_{i1})&= \beta \left[ \ln (y_{i0} - CV) - \ln (y_{i0})\right] \\ \frac{\gamma (r_{i0} - r_{i1}) }{\beta } + \ln (y_{i0})&= \ln (y_{i0} - CV) \\ \exp \left[ \frac{\gamma (r_{i0} - r_{i1}) }{\beta } + \ln (y_{i0}) \right]&= y_{i0} - CV \\ CV&= y_{i0} - \exp \left[ \frac{\gamma (r_{i0} - r_{i1}) }{\beta } + \ln (y_{i0}) \right] \\ CV&= y_{i0} - \exp \left[ \ln (y_{i0})\right] \exp \left[ \frac{\gamma (r_{i0} - r_{i1}) }{\beta } \right] \\ CV&= y_{i0} - y_{i0}\exp \left[ \frac{\gamma (r_{i0} - r_{i1}) }{\beta } \right] \\ CV&= y_{i0}\left[ 1 - \exp \left[ \frac{\gamma (r_{i0} - r_{i1}) }{\beta } \right] \right] \end{aligned} \end{aligned}$$

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sarrias, M., Jara, B. How Much Should We Pay for Mental Health Deterioration? The Subjective Monetary Value of Mental Health After the 27F Chilean Earthquake. J Happiness Stud 21, 843–875 (2020).

Download citation


  • Mental health
  • Subjective well-being
  • Life satisfaction approach
  • Economic valuation
  • Natural disasters
  • Monetary compensation
  • PTSD